Semantic parsing is a process that transforms natural language (NL) input (e.g., sentences, phrases, words, etc.) into computer executable complete meaning representations (MRs) for domain-specific applications, where Meaning Representation Language (MRL) for an application is assumed to be present. Part of the job of semantic parsing is to extract the domain information from a natural language input. For example, if a user queries “find Dixie's Grill,” a semantic parser should be able to determine that the user is looking for a restaurant named “Dixie's BBQ.”
Semantic parsing is a difficult computational problem. One reason for this is that NL inputs and their meanings may have many-to-many relationships. For example, multiple NL inputs may correspond to a single meaning, a single input could have different meanings (usually users intend to convey a single meaning, so system would need to interact with/learn from the user to disambiguate them), and other many-to-many relationships can occur. Conventional semantic parsers typically are inefficient at disambiguating a potential meaning of an NL input, particularly when the criteria size is large. Furthermore, conventional semantic parsers typically have sub-optimal matching capabilities. As a result, conventional semantic parsers do not scale well for complicated information domains and often produce inaccurate MRs.
Other types of information retrieval systems may scale better than some semantic parsers. For example, conventional Inverted-Index-Search (“IIS”) information retrieval systems, which are premised on keyword search, typically employ indexed information domains to facilitate efficient search. However, these systems typically ignore structural information (e.g., semantic structural information) that may be included in an NL input and therefore may not account for a user's intent. For example, for an NL input “find bus stop near VOICEBOX,” the results from a conventional IIS information retrieval system may relate to documents (e.g., webpages) that contain the keywords in the search input, without semantic information to help determine the intent of the user. Thus, the results may be irrelevant to the user's intended request.
Thus, what is needed is to improve semantic parsing for enhanced user intent recognition, improve the relevancy of information retrieval results, and improve recognition of NL, whether uttered, typed, or otherwise provided by a user. These and other problems exist.